Capital One offers a broad array of financial products and services to consumers, small businesses and commercial clients in the U.S., Canada and the UK. Capital One is a major partner in the design and creation of the FIRE steam Capital One Machine Learning .

UMD faculty and students will partner with Capital One research scientists in 2018 to study machine learning, data analytics and cybersecurity using diverse perspectives, technologies and applications. This new FIRE stream will allow students who have interest in these fields to start tackling critical challenges in these important areas of research.

About Machine Learning

Machine learning is a subdiscipline of computer science focused on the capacities for computer systems to learn without being purposefully programmed to perform specific tasks. These capacities derive from pattern recognition and artificial intelligence (AI). The Capital One Machine Learning research stream will focus on using machine learning to develop algorithms involved in predictive data analytics using both supervised and unsupervised approaches.

Potential Research Question - Fraudulent Activity Detection

Detection of fraudulent activity in commercial transactions presents a significant opportunity potentially worth billions of dollars per year. Creating automated systems that can detect fraud is a potential natural machine learning problem. Given historical transaction data, can we design an algorithm that learns to detect fraudulent transactions?

Such a system could use historical transaction data in a variety of ways:
  • Are there attributes of each specific transaction that could indicate fraud?
  • Are there regular patterns in a customer's transaction history that could help us to identify possible fraudulent transactions?
  • Can we leverage relationship data to detect fraudulent transactions?
  • How does a learning algorithm respond robustly to shifts in user behavior to avoid erroneously flagging transactions as fraudulent?
  • How do we ensure that systems that use machine learning are fair and unbiased in the predictions that they make?
  • Faculty Leader
    Dr. Jordan Boyd-Graber

    Research Educator
    Raymond Tu